diff options
| -rw-r--r-- | docs/sql-programming-guide.md | 60 | ||||
| -rw-r--r-- | python/pyspark/sql/_types.py | 2 | ||||
| -rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala | 14 |
3 files changed, 73 insertions, 3 deletions
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 6af10432b9..6b7b867ea6 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1594,6 +1594,64 @@ options. # Migration Guide +## Upgrading from Spark SQL 1.3 to 1.4 + +Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`. + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> +{% highlight scala %} + +// In 1.3.x, in order for the grouping column "department" to show up, +// it must be included explicitly as part of the agg function call. +df.groupBy("department").agg($"department", max("age"), sum("expense")) + +// In 1.4+, grouping column "department" is included automatically. +df.groupBy("department").agg(max("age"), sum("expense")) + +// Revert to 1.3 behavior (not retaining grouping column) by: +sqlContext.setConf("spark.sql.retainGroupColumns", "false") + +{% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> +{% highlight java %} + +// In 1.3.x, in order for the grouping column "department" to show up, +// it must be included explicitly as part of the agg function call. +df.groupBy("department").agg(col("department"), max("age"), sum("expense")); + +// In 1.4+, grouping column "department" is included automatically. +df.groupBy("department").agg(max("age"), sum("expense")); + +// Revert to 1.3 behavior (not retaining grouping column) by: +sqlContext.setConf("spark.sql.retainGroupColumns", "false"); + +{% endhighlight %} +</div> + +<div data-lang="python" markdown="1"> +{% highlight python %} + +import pyspark.sql.functions as func + +# In 1.3.x, in order for the grouping column "department" to show up, +# it must be included explicitly as part of the agg function call. +df.groupBy("department").agg("department"), func.max("age"), func.sum("expense")) + +# In 1.4+, grouping column "department" is included automatically. +df.groupBy("department").agg(func.max("age"), func.sum("expense")) + +# Revert to 1.3.x behavior (not retaining grouping column) by: +sqlContext.setConf("spark.sql.retainGroupColumns", "false") + +{% endhighlight %} +</div> + +</div> + + ## Upgrading from Spark SQL 1.0-1.2 to 1.3 In Spark 1.3 we removed the "Alpha" label from Spark SQL and as part of this did a cleanup of the @@ -1651,7 +1709,7 @@ moved into the udf object in `SQLContext`. <div class="codetabs"> <div data-lang="scala" markdown="1"> -{% highlight java %} +{% highlight scala %} sqlContext.udf.register("strLen", (s: String) => s.length()) diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py index fd98e116d2..b96851a174 100644 --- a/python/pyspark/sql/_types.py +++ b/python/pyspark/sql/_types.py @@ -1228,12 +1228,14 @@ class Row(tuple): raise AttributeError(item) def __reduce__(self): + """Returns a tuple so Python knows how to pickle Row.""" if hasattr(self, "__fields__"): return (_create_row, (self.__fields__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): + """Printable representation of Row used in Python REPL.""" if hasattr(self, "__fields__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) for k, v in zip(self.__fields__, tuple(self))) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala index 003a620dcc..543320e471 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala @@ -146,11 +146,21 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) * * // Scala: * import org.apache.spark.sql.functions._ - * df.groupBy("department").agg($"department", max($"age"), sum($"expense")) + * df.groupBy("department").agg(max("age"), sum("expense")) * * // Java: * import static org.apache.spark.sql.functions.*; - * df.groupBy("department").agg(col("department"), max(col("age")), sum(col("expense"))); + * df.groupBy("department").agg(max("age"), sum("expense")); + * }}} + * + * Note that before Spark 1.4, the default behavior is to NOT retain grouping columns. To change + * to that behavior, set config variable `spark.sql.retainGroupColumns` to `false`. + * {{{ + * // Scala, 1.3.x: + * df.groupBy("department").agg($"department", max("age"), sum("expense")) + * + * // Java, 1.3.x: + * df.groupBy("department").agg(col("department"), max("age"), sum("expense")); * }}} */ @scala.annotation.varargs |